As societies grow and expand in terms of intelligence and communications, the knowledge and information gained also grows, but at an exponential rate. Concomitant with the growth of societies, the need to exchange information also grows and expands.
Information transmitted, whether by light or electrical signals, must be converted into an appropriate format for transmission through conventional facilities. The transmission facilities normally include receivers which decode the information and convert it into an appropriate form for eventual use. The information is subject to corruption as a result of the transmission and receiving equipment, as well as by any transmission medium connected thereto.
Techniques are currently available for encoding information into a special format so that errors arising from the transmission of the coded information can be readily detected. In addition to error detection, some coding schemes also provide for error correction. These error detection and correction systems operate satisfactorily when only a few errors occur. However, in noisy environments where the corruption of the coded information becomes appreciable, no practical error correction scheme is available. While error detection and correction schemes are theoretically feasible in systems incurring high transmission error rates, the practical shortcoming is that processing systems currently available cannot perform the extraction of error-free messages from a highly corrupted encoded signal in reasonable periods of time. See P. 27 "Error-Control Techniques for Digital Communication" Arnold M. Michelson, Allen H. Levesque, John Wiley & Sons, New York 1985 ISBN 0-471-88074-4. The real time error correction problem is further exacerbated in high speed information transmission systems, such as is typical in current computer and telecommunication transmission systems.
Neural networks are the subject of contemporary theorizing in connection with pattern recognition. Pattern recognition systems are capable of restoring or recognizing corrupted images or patterns if sufficient artifacts of the representation are available to provide the true representation of the image. In this respect, a neural network is fault tolerant in that the image can be significantly perturbated, but yet after being processed through the neural network the image can be restored. Such systems are also referred to as content addressable memories or fuzzy pattern recognition systems.
Typically, neural networks comprise analog neuron circuits having multiple inputs and an output connected to the inputs of other similar circuits. Such a system is termed "massively parallel" because of the large number of interconnections of such parallel-acting neurons forming a circuit matrix. As is well documented in the literature, a neural network generally comprises a set of nonlinear summing amplifiers coupled by an adjustable connection matrix. The values of the interconnection matrix elements are proportional to the autocorrelation matrix associated with the desired stable states of the network. The strength of a network connection can be expressed mathematically as: ##EQU1## where V.sup.s are the stored states of the network, and V.sub.i.sup.s is the ith component of the Sth state stored. The nonlinear amplifiers are constructed using zero crossing comparators.
While neural networks theoretically represent a solution to correlation problems, such networks are not practically feasible with today's technology because of the massive innerconnection problem. The innerconnection problem only worsens when integrated circuits are scaled to provide high density packing, which is the current trend with microelectronics.
From the foregoing, it can be seen that a need exists for a computational network which can perform associative and correlation functions in accordance with current digital processing methods and apparatus. More particularly, a need exists for a digital systolic processor which can perform high speed correlations and associations of high speed serial data with neural network matrix connections using a conventional random access memory. Another need exists for providing a method of applying the attributes of neural networks to new applications not heretofore attempted.